{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpolating Network Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Frequently a set of `Networks` is recorded while changing some other parameters; like temperature, voltage, current, etc. Once this set of data acquired, it is sometime usefull to estimate the behaviour of the network for parameter values that lie in between those that have been mesured. For that purpose, interpolating a network from a set of network is possible. This example demonstrates how to do this using [NetworkSets](../../tutorials/NetworkSet.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JH218595\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\style\\core.py:112: UserWarning: Style includes a parameter, 'interactive', that is not related to style.  Ignoring\n",
      "  _apply_style(rc)\n"
     ]
    }
   ],
   "source": [
    "import skrf as rf \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "rf.stylely()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Narda 3752 phase shifter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we are characterizing a old [narda phase shifter 3752](https://nardamiteq.com/docs/119-PHASESHIFTERS.PDF) at 1.5 GHz. \n",
    "![narda 3752 phase shifter](phase_shifter_measurements/Narda_3752.jpg) :\n",
    "\n",
    "In order to deduce the phase shift that one can obtain at this specific frequency, we have measured scattering parameters in the 1-2 GHz band at 19 positions of the phase knob (from 0 to 180). These measurements are loaded into a [NetworkSets](../../tutorials/NetworkSet.ipynb) object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Array containing the 19 phase shift indicator values\n",
    "indicators_mes = np.linspace(0, 180, num=19)  # from 0 to 180 per 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ntw_set contains 19 networks\n"
     ]
    }
   ],
   "source": [
    "ntw_set = rf.NetworkSet.from_zip('phase_shifter_measurements/phase_shifter_measurements.zip')\n",
    "print('ntw_set contains', len(ntw_set), 'networks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract from the network set the phase shift and $S_{11}$ at the specific frequency of 1.5 GHz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = '1.5 GHz'\n",
    "phases_mes = np.squeeze([ntw[f].s21.s_deg for ntw in ntw_set])\n",
    "s11_mes = np.squeeze([ntw[f].s11.s_db for ntw in ntw_set])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like however to get the phase shift values for intermediate settings of this phase shifter. To that purpose, let's create a network from the interpolation of the measured networks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have interpolated the network for 181 points\n"
     ]
    }
   ],
   "source": [
    "# the indicator values for which we want to interpolate the network\n",
    "indicators = np.linspace(0, 180, num=181)  # every degrees for 0 to 180\n",
    "\n",
    "phases_interp = [ntw_set.interpolate_from_network(indicators_mes, phi)['1.5GHz'].s21.s_deg for phi in indicators]\n",
    "phases_interp = np.squeeze(phases_interp)\n",
    "\n",
    "s11_interp = [ntw_set.interpolate_from_network(indicators_mes, phi, interp_kind='quadratic')['1.5GHz'].s11.s_db for phi in indicators]\n",
    "s11_interp = np.squeeze(s11_interp)\n",
    "\n",
    "print('We have interpolated the network for', len(phases_interp), 'points')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2,1, figsize=(10,7), sharex=True)\n",
    "ax1.set_title('Narda Phase Shifter vs Indicator @ 1.5 GHz', fontsize=10)\n",
    "ax1.plot(indicators, s11_interp, label='Interpolated network')\n",
    "ax1.plot(indicators_mes, s11_mes, '.', ms=10, label='Measurements')\n",
    "ax1.legend()\n",
    "ax1.set_ylabel(r'$S_{11}$ [dB]')\n",
    "\n",
    "ax2.plot(indicators, phases_interp, label='Interpolated network')\n",
    "ax2.plot(indicators_mes, phases_mes, '.', ms=10, label='Measurements')\n",
    "ax2.set_xlabel('Phase shifter indicator')\n",
    "ax2.set_ylabel('$S_{21}$ [deg]')\n",
    "\n",
    "ax2.fill_between(indicators, phases_mes[0], phases_mes[-1], alpha=0.2, color='r')\n",
    "ax2.text(40, 25, 'not available zone')\n",
    "ax2.legend()\n",
    "ax2.set_ylim(-200, 200)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
